Multi-Computer System for Optimized Queue Management Based on Facial Recognition

ABSTRACT

Arrangements for smart tracking and queue management are provided. In some aspects, in response to detecting a user at an entity location, image data may be captured of the user. The image data may be analyzed using one or more facial recognition techniques to determine whether the user is a recognized user. In some examples, a user or user device may be connected to an entity computing device associated with a shopping cart or other device for capturing items for purchase. The user may gather items for purchase and purchase item data may be transmitted for analysis. In some examples, a request to checkout may be received and, in response, real-time queue data may be requested. The real-time queue data may be analyzed using a machine learning model to determine an optimal queue for the user. A notification identifying the queue may be transmitted to the entity computing device.

BACKGROUND

Aspects of the disclosure relate to electrical computers, systems, anddevices for dynamic authentication and queue management based on facialrecognition data.

People are often looking to streamline necessary tasks and spend lesstime on things like errands. Accordingly, while errands such as groceryshopping may be a necessary part of life for many, conventionalarrangements for shopping, checkout, and the like, are inefficient, relyon inaccurate data or are essentially just manual processes.Accordingly, arrangements discussed herein rely on facial recognition toidentify a customer, perform smart tracking operations as the user movesthrough a location, execute dynamic authentication processes, andgenerate a recommended optimal queue based on real-time data.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosure. The summary is not anextensive overview of the disclosure. It is neither intended to identifykey or critical elements of the disclosure nor to delineate the scope ofthe disclosure. The following summary merely presents some concepts ofthe disclosure in a simplified form as a prelude to the descriptionbelow.

Aspects of the disclosure provide effective, efficient, scalable, andconvenient technical solutions that address and overcome the technicalissues associated with efficiently and quickly capturing customerpurchases and processing user transactions.

In some aspects, a user may be detected at an entity location. Forinstance, a user computing device may detect a signal emitted by alocation beacon at the entity location. Responsive to detecting theuser, image data may be captured of the user. The image data may beanalyzed using one or more facial recognition techniques to determinewhether the user is a recognized user. If so, smart tracking and optimalqueue functions may be initiated.

In some examples, a user or user device may be connected to an entitycomputing device associated with a shopping cart or other device forcapturing items for purchase. The user may gather items for purchase andpurchase item data may be transmitted for analysis. In some examples, auser may request checkout (e.g., via an interface on the entitycomputing device). In response to receiving the request to checkout,real-time queue data may be requested. The real-time queue data mayinclude image data, weight data, and the like. The real-time queue datamay be analyzed using a machine learning model to determine an optimalor recommended queue for the user. The user may then proceed to thequeue and, location data may be used to gather checkout data such aswhether the user accepted the recommended queue, amount of time tocheckout, number of items processed, and the like. This information maybe used to update and/or validate the machine learning model.

These features, along with many others, are discussed in greater detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIGS. 1A and 1B depict an illustrative computing environment forimplementing smart tracking, dynamic authentication and queue managementfunctions in accordance with one or more aspects described herein;

FIGS. 2A-2J depict an illustrative event sequence for implementing smarttracking, dynamic authentication and queue management functions inaccordance with one or more aspects described herein;

FIG. 3 illustrates an illustrative method for implementing smarttracking and queue management functions according to one or more aspectsdescribed herein;

FIG. 4 illustrates an illustrative method for implementing dynamicauthentication and smart tracking functions according to one or moreaspects described herein;

FIGS. 5 and 6 illustrate example user interfaces that may be generatedaccording to open or more aspects described herein; and

FIG. 7 illustrates one example environment in which various aspects ofthe disclosure may be implemented in accordance with one or more aspectsdescribed herein.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which aspects of the disclosure may be practiced. It isto be understood that other embodiments may be utilized, and structuraland functional modifications may be made, without departing from thescope of the present disclosure.

It is noted that various connections between elements are discussed inthe following description. It is noted that these connections aregeneral and, unless specified otherwise, may be direct or indirect,wired or wireless, and that the specification is not intended to belimiting in this respect.

As discussed above, various day-to-day functions and errands can be timeconsuming and inefficient. For instance, tasks such as grocery shoppingare often inefficient because the checkout process can be cumbersome.Accordingly, aspects described herein provide smart tracking, dynamicauthentication and queue management functions to improve the process.

For instance, a user may be detected upon entering an entity location.An image of the user may be captured and facial recognition analysis maybe performed to determine whether the user is a recognized user. If so,a user profile may be retrieved.

In some examples, detecting the user may include detecting a usercomputing device and connecting an entity point-of-sale system to theuser computing device. The type of connection between the devices may beidentified and used to determine authentication requirements.

The user may be associated with an entity computing device that may beconnected to, for instance, a shopping cart or other device forcapturing goods for purchase. The purchase item data may be captured andtransmitted to the computing platform for processing.

Upon receiving a request to checkout (e.g., via the entity computingdevice), real-time queue data may be requested and received. Thereal-time queue data, as well as location of the entity computing deviceassociated with the user and purchase item data may be used as inputs ina machine learning model. The machine learning model may analyze thedata and identify and optimal queue for the user. The optimal queue maybe transmitted to the user and displayed on the entity computing deviceassociated with the user, on a user computing device, or the like.

These and various other arrangements will be discussed more fully below.

FIGS. 1A-1B depict an illustrative computing environment forimplementing and using facial recognition for smart tracking and queuemanagement in accordance with one or more aspects described herein.Referring to FIG. 1A, computing environment 100 may include one or morecomputing devices and/or other computing systems. For example, computingenvironment 100 may include smart tracking computing platform 110,internal entity computing system 125, entity point-of-sale (POS) system160, image capture device 165, user computing device 170, entitycomputing device 150, and entity computing device 155. Although oneinternal entity computing system 125, one POS system 160, one imagecapture device 165, one user computing device 170 and two entitycomputing devices 150, 155 are shown, any number of systems or devicesmay be used without departing from the invention.

Further, while aspects may be described in the context of processesperformed by the smart tracking computing platform 110, in someexamples, one or more processes or functions described may be performedby the entity POS system 160, by the entity POS system 160 incombination with the smart tracking computing platform 110, or the like.Additionally or alternatively, smart tracking computing platform 110 maybe part of (e.g., a single physical device or a connected device) theentity POS system 160 in some examples.

Smart tracking computing platform 110 may be configured to performintelligent, dynamic and efficient smart tracking and queue optimizationor management based on facial recognition. For instance, upon detectinga registered user at a location (e.g., based on location beaconsdetecting a cell phone signal, based on a POS device detecting a mobiledevice signal, or the like), smart tracking computing platform 110 maygenerate an instruction to capture image data. The instruction may betransmitted to the entity POS system 160 at the location and may causeactivation of an image capture device 165 associated with the entity POSsystem 160. The image capture device 165 may capture an image of theuser and transmit it (e.g., via the entity POS system 160) to the smarttracking computing platform 110 for analysis.

Smart tracking computing platform 110 may receive the image data and mayperform facial recognition analysis to determine whether the user is arecognized user. If so, user profile data may be retrieved. In someexamples, the user profile data may include identifying data of theuser, contact information, user device data and/or identifiers, as wellas payment data (e.g., one or more accounts, debit cards, credit cards,or the like for use in executing or processing transactions).

In some examples, retrieval of the user profile data may initiate one ormore smart tracking and optimized queue management features.

In some examples, the user may be authenticated (e.g., via the entitycomputing device 150 and/or a mobile user device such as user computingdevice 170). In some arrangements, authentication data required or typeof authentication may be based on a type of communication orcommunication protocol. For instance, the entity POS system 160 mayconnect to the user computing device 170 upon detecting the device. Thetype of connection may then dictate the authentication method, type ofdata, number of factors, or the like, to authenticate the user. In someexamples, the type of connection may also dictate data transmissionprotocols.

In one example, if the entity POS system 160 and/or user computingdevice 170 are communicating via ultra-wide band, a first type ofauthentication method or data may be required. If the device iscommunicating via Bluetooth™ a second type of authentication method ordata may be required. Accordingly, the system may identify the type ofconnection or communication protocol currently in use and identify atype of authentication method or particular types of authentication datarequired to authenticate the user based on the type of connection. Insome examples, the authentication method associated with each type ofconnection may be user selected and stored in, for instance the userprofile. Additionally or alternatively, the authentication requirementsmay be determined by an enterprise organization or by the smart trackingcomputing platform 110.

Upon authentication, the user may be associated with an entity computingdevice, such as entity computing device 150. The entity computing devicemay, in some examples, be connected to or otherwise associated with anentity device, such as a shopping cart. The entity computing device 150may provide data to the smart tracking computing platform 110 related tolocation of the device 150 and user, weight of items in the device,number of items in the device, and the like. The entity computing device150 may include a display screen that may display one or more userinterfaces.

In some examples, a user may request checkout via the display of theentity computing device 150. Accordingly, an instruction may betransmitted to smart tracking computing platform 110 to initiate queuemanagement and optimization functions. For instance, real-time queuedata may be received from each active queue at the entity location. Thequeue data may include image data of a line, image data of itemsassociated with people in line, weight data associated with weight ofitems being checked out, number of items associated with each user inline, and the like. This data may be analyzed in real-time to identify arecommended queue for the user. The recommended queue may be transmittedto the entity computing device 150 and/or user computing device 170 anddisplayed by the display.

In some arrangements, location data of the user may be received todetermine whether the user accepted the recommended queue. This feedbackdata may be used to further refine, for instance, machine learningmodels used to evaluate real-time queue data to generate a recommendedqueue.

In some examples, based on authentication data received from the user,an automatic payment process may be initiated. Accordingly, the user maypay for purchased items with minimal or no user interaction (e.g., uponexpiration of a predetermined time after scanning an item at the queue,upon user input indicating completion of the scanning process, or thelike). Payment information may, in some examples, be retrieved from userprofile data.

Computing environment 100 may further include internal entity computingsystem 125. Internal entity computing system 125 may host one or moreapplications, store user information such as account or paymentinformation, and the like. In some examples, data from internal entitycomputing system 125 may be retrieved to perform payment processingbased on identification of the user from facial recognition.

Computing environment 100 may further include one or more entitycomputing devices 150, 155. Entity computing devices 150, 155 may bemobile devices that may, in some examples, be connected to or associatedwith an item at an entity location, such as a shopping cart. The entitycomputing devices 150, 155 may include a touch screen or otherinteractive display for displaying notifications to users, receivinguser input or response data, and the like.

Computing environment 100 may further include an entity point-of sale(POS) system 160. The entity POS system 160 may be one of a plurality ofPOS systems 160 each associated with a checkout queue at the entitylocation. In other examples, the entity location may have a single POSsystem and a plurality of terminals connected thereto and distributed atvarious checkout queues at the entity location. The POS system 160 mayinclude one or more image capture devices 165. For instance, an imagecapture device 165 may be arranged at or near an entrance to the entitylocation to capture user image data that may then be processed usingfacial recognition.

Further, image capture devices 165 may be distributed at the checkoutqueues to capture real-time image data associated with a number of usersin a queue, number of items associated with each user, and the like.This data may be analyzed (e.g., using machine learning) to generate arecommended queue for a user.

As mentioned above, computing environment 100 also may include one ormore networks, which may interconnect one or more of smart trackingcomputing platform 110, internal entity computing system 125, entity POSsystem 160, image capture device 165, entity computing device 150,entity computing device 155, and/or user computing device 170. Forexample, computing environment 100 may include private network 190 andpublic network 195. Private network 190 and/or public network 195 mayinclude one or more sub-networks (e.g., Local Area Networks (LANs), WideArea Networks (WANs), or the like). Private network 190 may beassociated with a particular organization (e.g., a corporation,financial institution, educational institution, governmentalinstitution, or the like) and may interconnect one or more computingdevices associated with the organization. For example, smart trackingcomputing platform 110 and internal entity computing system 125 may beassociated with an enterprise organization (e.g., a financialinstitution), and private network 190 may be associated with and/oroperated by the organization, and may include one or more networks(e.g., LANs, WANs, virtual private networks (VPNs), or the like) thatinterconnect smart tracking computing platform 110, internal entitycomputing system 125, and one or more other computing devices and/orcomputer systems that are used by, operated by, and/or otherwiseassociated with the organization. Public network 195 may connect privatenetwork 190 and/or one or more computing devices connected thereto(e.g., smart tracking computing platform 110, internal entity computingsystem 125,) with one or more networks and/or computing devices that arenot associated with the organization. For example, entity POS system160, image capture device 165, entity computing device 150, entitycomputing device 155, and/or user computing device 170, might not beassociated with an organization that operates private network 190 (e.g.,because entity POS system 160, image capture device 165, entitycomputing device 150, entity computing device 155, and/or user computingdevice 170 may be owned, operated, and/or serviced by one or moreentities different from the organization that operates private network190, one or more customers of the organization, one or more employees ofthe organization, public or government entities, and/or vendors of theorganization, rather than being owned and/or operated by theorganization itself), and public network 195 may include one or morenetworks (e.g., the internet) that connect entity POS system 160, imagecapture device 165, entity computing device 150, entity computing device155, and/or user computing device 170 to private network 190 and/or oneor more computing devices connected thereto (e.g., smart trackingcomputing platform 110, internal entity computing system 125).

Referring to FIG. 1B, smart tracking computing platform 110 may includeone or more processors 111, memory 112, and communication interface 113.A data bus may interconnect processor(s) 111, memory 112, andcommunication interface 113. Communication interface 113 may be anetwork interface configured to support communication between smarttracking computing platform 110 and one or more networks (e.g., privatenetwork 190, public network, or the like). Memory 112 may include one ormore program modules having instructions that when executed byprocessor(s) 111 cause smart tracking computing platform 110 to performone or more functions described herein and/or one or more databases thatmay store and/or otherwise maintain information which may be used bysuch program modules and/or processor(s) 111. In some instances, the oneor more program modules and/or databases may be stored by and/ormaintained in different memory units of smart tracking computingplatform 110 and/or by different computing devices that may form and/orotherwise make up smart tracking computing platform 110.

For example, memory 112 may have, store and/or include image dataanalysis module 112 a. Image data analysis module 112 a may storeinstructions and/or data that may cause or enable the smart trackingcomputing platform 110 to store a plurality of user images associatedwith registered users, receive image data from, for instance, imagecapture device 165 upon detecting a user at an entity location, andexecute one or more facial recognition analysis functions on the imagedata to determine whether the user is a known or recognized user. Facialrecognition techniques employing various recognition algorithms may beused. For instance, geometric algorithms may be used that evaluatedistinguishing features within an image and/or on a person and comparethose features to features of pre-stored images. In another example,photo-metric algorithms may be used to associate values with differentaspects of an image and compare those values to one or more templates toeliminate variances. Some example algorithms that may be used mayinclude principal component analysis, linear discriminant analysis,elastic bunch graph matching, hidden Markov model, multilinear subspacelearning, and/or dynamic link matching. In some examples, threedimensional recognition and/or thermal cameras may be used.

Smart tracking computing platform 110 may further have, store and/orinclude user profile module 112 b. User profile module 112 b may storeinstructions and/or data that may cause or enable the smart trackingcomputing platform 110 to store one or more user profiles based oncustomer registration data, customer visits to one or more entitylocations, and the like. The user profile may include name, contactinformation including phone number associated with a mobile device suchas user computing device 170, or the like. In some examples, the userprofile may include one or more pre-stored images of the user (e.g.,captured during, for instance, a registration process). In somearrangements, a user may provide payment information, such as a debitcard, credit card, or the like, that may be used to process the paymentor transaction. In some examples, the user profile may further includeprestored authentication data (e.g., for comparison to authenticationresponse data to authenticate a user), authentication requirements basedon connection type, and the like.

Smart tracking computing platform 110 may further have, store and/orinclude notification generation module 112 c. Notification generationmodule 112 c may store instructions and/or data that may cause or enablethe smart tracking computing platform 110 to generate one or morenotifications associated with requesting checkout, a recommended oroptimal queue, or the like.

Smart tracking computing platform 110 may further have, store and/orinclude communication/authentication control module 112 d.Communication/authentication control module 112 d may store instructionsand/or data that may cause or enable the smart tracking computingplatform 110 to receive information associated with a connection type(e.g., ultra-wideband, Bluetooth, near-field, or the like) and identifyone or more authentication requirements, data transmission requirements,or the like. For instance, upon receiving information related to aconnection type (e.g., between entity POS system 160 and, for instance,user computing device 170), communication/authentication control module112 d may identify authentication requirements, data transmissionrequirements, and the like, and may transmit the requirements to theentity POS system 160, user computing device 170, or the like.

Smart tracking computing platform 110 may further have, store and/orinclude machine learning engine 112 e. Machine learning engine 112 e maystore instructions and/or data that may cause or enable the smarttracking computing platform 110 to train, execute, update and/orvalidate a machine learning model. The machine learning model may betrained using historical data including labeled datasets identifyingpatterns or sequences or data. For instance, the machine learning modelmay be trained using historical data related to number of people in aqueue, number of items in a queue, time to complete processing, and thelike. Accordingly, the machine learning model may receive, as inputs,real-time queue data, purchase item data for the items being purchased,location data of the user (e.g., distance from one or more queues), andthe like, and identify an optimal or recommended queue for the user.

Various machine learning algorithms may be used (e.g., by the machinelearning engine 112 e and/or the one or more machine learning models)without departing from the invention, such as supervised learningalgorithms, unsupervised learning algorithms, regression algorithms(e.g., linear regression, logistic regression, and the like), instancebased algorithms (e.g., learning vector quantization, locally weightedlearning, and the like), regularization algorithms (e.g., ridgeregression, least-angle regression, and the like), decision treealgorithms, Bayesian algorithms, clustering algorithms, artificialneural network algorithms, and the like. Additional or alternativemachine learning algorithms may be used without departing from theinvention.

Smart tracking computing platform 110 may further have, store, and/orinclude queue data analysis module 112 f. Queue data analysis module 112f may store instructions and/or data that may cause or enable the smarttracking computing platform 110 to generate one or more requests forreal-time queue data, in conjunction with the machine learning engine112 e, analyze the received real-time queue data to identify arecommended or optimal queue, and the like.

Smart tracking computing platform 110 may further have, store and/orinclude a database 112 g. Database 112 g may store data associated withusers, historical data associated with previous queue recommendations,and the like.

FIGS. 2A-2J depict one example illustrative event sequence for usingsmart tracking and queue management functions in accordance with one ormore aspects described herein. The events shown in the illustrativeevent sequence are merely one example sequence and additional events maybe added, or events may be omitted, without departing from theinvention. Further, one or more processes discussed with respect toFIGS. 2A-2J may be performed in real-time or near real-time.

Referring to FIG. 2A, at step 201, a user device may be detected. Forinstance, user computing device 170 may be detected by an entitypoint-of-sale (POS) system, such as entity point-of-sale system 160. Insome examples, entity POS system 160 may continuously scan for deviceswithin a predefined range (e.g., to identify users who are, entering alocation, purchasing items, or the like) and may detect a signal emittedfrom user computing device 170. In another example, entity POS 160 mayemit a signal that may be detected by user computing device 170 whichmay then cause transmission of a message indicating a presence of usercomputing device 170 within a predetermined range of the entity POS 160.In some examples, one or more location beacons may be used to transmitand/or detect signals and associated user computing devices.

Although the arrangements discussed herein describe a user device beingdetected by entity POS system 160, in some examples, the user device maybe detected by smart tracking computing platform 110 and/or smarttracking computing platform 110 may be same device or in communicationwith entity POS system 160, without departing from the invention.

At step 202, a connection may be established between entity POS system160 and image capture device 165. For instance, a first wirelessconnection may be established between the entity POS system 160 andimage capture device 165. Upon establishing the first wirelessconnection, a communication session may be initiated between entity POSsystem 160 and image capture device 165.

At step 203, entity POS system 160 may generate one or more instructionsto capture an image. For instance, entity POS system 160 may generate aninstruction to capture an image of a user who may be positioned in frontof or near the image capture device and/or a point-of-sale system of themerchant, an entry to the location, or the like.

Although the arrangements discussed include instructions generated byand received from the entity POS system 160, smart tracking computingplatform 110 may generate and transmit instructions to capture an imagewithout departing from the invention. Additionally or alternatively,smart tracking computing platform 110 and entity POS system 160 may be asame device or in communication.

At step 204, the generated instruction to capture an image may betransmitted by the entity POS system 160 to the image capture device165. For instance, the instruction to capture the image may betransmitted during the communication session initiated upon establishingthe first wireless connection.

At step 205, the image capture device 165 may receive the instruction tocapture an image and may execute one or more image capture instructionsor commands to capture the image. In some examples, the image captureinstructions may include a direction, area, location, or the like, ofthe user being captured. Accordingly, the image capture device 165 maymodify a position (e.g., tilt, rotate, or the like) to capture thedesired image.

At step 206, the captured image may be transmitted to the entity POSsystem 160. For instance, the captured image may be transmitted by theimage capture device 165 to the entity POS system 160 during thecommunication session initiated upon establishing the first wirelessconnection.

With reference to FIG. 2B, at step 207, the entity POS system 160 mayreceive the captured image. Responsive to receiving the captured image,the entity POS system 160 may establish a connection with the usercomputing device 170 at step 208. For instance, a connection may beestablished between entity POS system 160 and user computing device 170.For instance, a second wireless connection may be established betweenthe entity POS system 160 and user computing device 170. Uponestablishing the second wireless connection, a communication session maybe initiated between entity POS system 160 and user computing device170.

The connection established between entity POS system 160 and usercomputing device may be established via one or more different channels,communication protocols, technologies, or the like. For instance, theconnection between the entity POS system 160 and the user computingdevice 170 may be formed via Bluetooth™, ultra-wideband (UWB),near-field communication, or the like. In some examples, datatransmitted via the second wireless connection may be encrypted orotherwise secured or protected to avoid unauthorized actors accessingthe data being transferred. In some examples, different types of datamay be transferred based on the communication protocol or technologyforming the connection between the entity POS system 160 and the usercomputing device 170. Additionally or alternatively, one or moreauthentication techniques may be based on the communication protocol ortechnology forming the connection. The type of connection may be used toidentify data transfer protocols, authentication requirements, and thelike.

At step 209, a connection may be established between entity POS system160 and smart tracking computing platform 110. For instance, a thirdwireless connection may be established between the entity POS system 160and smart tracking computing platform 110. Upon establishing the thirdwireless connection, a communication session may be initiated betweenentity POS system 160 and smart tracking computing platform 110.

At step 210, the received image data and data or information associatedwith the connection formed between the user computing device 170 and theentity POS system 160 may be transmitted to the smart tracking computingplatform 110. For instance, a type of connection (e.g., communicationprotocol or technology used for the connection) may be transmitted withthe image data to the smart tracking computing platform 110.

At step 211, the image data and connection information may be receivedby the smart tracking computing platform 110 and analyzed to determineauthentication requirements, data transmission requirements, and thelike. For instance, based on the type of connection, differentauthentication and/or data transmission requirements may be executed. Insome examples, one or more connection types may be more prone tounauthorized activity than others. Accordingly, modifications toauthentication requirements and/or data transmission requirements orprotocols may be made based on the type of connection. In one example, afirst authentication requirement (e.g., username and password) may berequired for a first type of connection (e.g., Bluetooth™) while asecond, different authentication requirement (e.g., two factorauthentication, biometric data, or the like) may be required for asecond, different type of connection (e.g., ultra-wideband). Variousother examples of modified authentication requirements may be usedwithout departing from the invention.

At step 212, the requirements may be transmitted to the user computingdevice 170 and, with reference to FIG. 2C, at step 213, the requirementsmay be transmitted to the entity POS system 160. For instance, the datatransmission and/or authentication requirements may be transmitted toboth the user computing device 170 (e.g., based on the user computingdevice being registered with the smart tracking computing platform 110and identified via the connection information) and the entity POS system160 for execution.

At step 214, the entity POS system 160 may receive and execute the datatransmission requirements and/or authentication requirements. Forinstance, at step 215, a request for authentication data may begenerated. The request for authentication data may be based on theauthentication requirements determined based on the connection type andreceived from the smart tracking computing platform 110.

At step 216, the request for authentication data may be transmitted bythe entity POS system 160 to the user computing device 170.

At step 217, the request for authentication data may be received by usercomputing device 170 and displayed by a display of the device. Userauthentication response data may be received via the user computingdevice 170. For instance, the user may provide (e.g., via an interactiveuser interface displayed by the display of the user computing device170) user input including the requested authentication data. This userinput may be used to generate user authentication response data.

At step 218, the user authentication response data may be transmitted bythe user computing device 170 to the entity POS system 160.

With reference to FIG. 2D, at step 219, the authentication response datamay be received by the entity POS system 160 and, in some examples, atstep 220, transmitted to the smart tracking computing platform 110 foranalysis.

At step 221, the image data may be analyzed using one or more facialrecognition techniques. For instance, facial recognition techniquesemploying various recognition algorithms may be used. For instance,geometric algorithms may be used that evaluate distinguishing featureswithin an image and/or on a person and compare those features tofeatures of pre-stored images. In another example, photo-metricalgorithms may be used to associate values with different aspects of animage and compare those values to one or more templates to eliminatevariances. Some example algorithms that may be used may includeprincipal component analysis, linear discriminant analysis, elasticbunch graph matching, hidden Markov model, multilinear subspacelearning, and/or dynamic link matching. In some examples, threedimensional recognition and/or thermal cameras may be used.

In some examples, analyzing the image data may include comparing theimage data to pre-stored image data (e.g., image data of a user capturedduring, for instance, a registration process). If a match occurs, theuser may be identified and a user profile associated with the identifieduser may be retrieved. In some examples, the user profile data mayinclude identifying information, pre-stored authentication data, userdevice data, user payment data, and the like.

At step 222, the user may be authenticated by the smart trackingcomputing platform 110. For instance, the authentication response datamay be compared to pre-stored authentication data in the user profile.If the data matches, the user may be authenticated. If the data does notmatch, additional authentication data may be requested and/or the usermay be notified that smart tracking is not available for this trip. Insome examples, authenticating the user may include confirming that theauthentication response data meets the identified authenticationrequirements (e.g., based on connection type).

At step 223, the authentication output (e.g., whether the authenticationresponse data matches the pre-stored authentication data) and, if theuser is authenticated, the user profile, may be transmitted by the smarttracking computing platform 110 to the entity POS system 160. In someexamples, the authentication output may include one or more instructionsor commands to cause a connection to be established between devices,such as user computing device 170 and entity computing device 150. Atstep 224, entity POS system 160 may receive the authentication outputand user profile data.

With reference to FIG. 2E, at step 225, the entity POS system 160 maycause a connection to be established between the user computing device170 and the entity computing device 150 (for example, cause the devicesto be paired). For instance, a plurality of entity computing devices 150may be associated with the entity, entity location, and the like. Theplurality of devices may be associated with, linked to or incommunication with the entity POS system 160. In some examples, eachdevice of the plurality of entity devices may be associated with adevice within the entity, such as a shopping cart. Accordingly, as theuser moves through the entity location and retrieves items for purchase,they will be placed in the shopping cart and the associated entitycomputing device, e.g., entity computing device 150, may capture theitem (e.g., image of the item, stock keeping unit (SKU), or the like)and may add it to a list of items being purchased. In some examples,additional details about the item may be retrieved (e.g., from adatabase storing information about the items) such as weight, size ofitem or container, or the like.

In some examples, each entity computing device 150, 155 and/or shoppingcart associated therewith may have a sensor provisioned with, forexample, Bluetooth low energy. The entity computing devices 150, 155and/or shopping carts may be integrated with the entity POS system 160in a mesh network using, in some examples, ultra-wideband (UWB) that mayenable precise location determination or calculation for optimal queuedecisioning.

Accordingly, at step 225, the user computing device 170 may be connectedto the entity computing device 150 associated with the shopping cartbeing used by a user associated with the user computing device 170. Insome examples, the connection may be established and data may betransferred according to data transfer protocols established oridentified based on the type of connection. For instance, if theconnection between the entity POS system 160 and the user computingdevice 170 was established via a first type of connection (e.g., UWB),the entity computing device 150 may be connected to the user computingdevice 170 via the same type of connection (e.g., UWB) in order toexecute the data transfer protocols, authentication requirements, andthe like, associated with that type of connection and executed by theentity POS system 160 and user computing device 170.

At step 226, the smart tracking computing platform 110 may initiate oneor more smart tracking and queue management functions (e.g., based onthe user computing device 170 connecting to the entity computing device150). For instance, one or more item tracking, pricing, locationtracking, or the like, functions may be initiated, activated, or thelike.

At step 227, selected item data may be received or captured by theentity computing device 150. For instance, as the user proceeds to shop,the user may places one or more items in the shopping cart associatedwith the entity computing device 150. The entity computing device 150may capture that information.

At step 228, a connection may be established between entity computingdevice 150 and smart tracking computing platform 110. For instance, afourth wireless connection may be established between the entitycomputing device 150 and smart tracking computing platform 110. Uponestablishing the fourth wireless connection, a communication session maybe initiated between entity computing device 150 and smart trackingcomputing platform 110.

At step 229, the captured selected item data may be transmitted by theentity computing device 150 to the smart tracking computing platform110. For instance, the selected item data may be transmitted during thecommunication session initiated upon establishing the fourth wirelessconnection.

With reference to FIG. 2F, at step 230, the smart tracking computingplatform 110 may receive the selected item data.

At step 231, the entity computing device 150 may receive a request foroptimal checkout. For instance, the entity computing device 150 mayinclude a display providing a user interface including selectableoptions for the user. FIG. 5 illustrates one example user interface 500.As shown in FIG. 5 , one option may include an option to checkout andidentify an optimal checkout queue for the user. Accordingly, when theuser has completed the shopping process, they may select an optionindicating they are ready to check out and an optimal queue may beidentified for the user.

At step 232, the request for optimal checkout may be transmitted by theentity computing device 150 to the smart tracking computing platform110. For instance, the request for optimal checkout may be transmittedduring the communication session initiated upon establishing the fourthwireless connection.

Although arrangements shown including transmitting selected item dataseparately from the request for optimal checkout, in some examples, theselected item data may be transmitted to the smart tracking computingplatform 110 with the request for optimal checkout.

At step 233, the smart tracking computing platform 110 may receive therequest for optimal checkout and may initiate optimal checkoutfunctions.

At step 234, a request for real-time queue data may be generated by thesmart tracking computing platform 110. For instance, a request forreal-time data associated with each queue at the entity location may begenerated.

At step 235, the smart tracking computing platform 110 may transmit therequest for real-time queue data to the entity POS system 160.

With reference to FIG. 2G, at step 236, the entity POS system 160 mayreceive the request for real-time queue data and may execute therequest.

At step 237, an instruction to capture real-time queue data may begenerated by the entity POS system 160. In some examples, the request tocapture real-time queue data may include a request to capture image dataat various locations throughout the entity location. For instance, ifeach queue has a camera associated with it, the instruction to capturereal-time queue data may include an instruction to capture an image of acurrent queue. In some examples, the instruction to capture real-timequeue data may include an instruction to capture a weight associatedwith items on a conveyor belt at each queue (e.g., based on a weightsensor associated with the queue). This data may be used to determine alength of time checkout will take for current items. The request forreal-time queue data may include requests for other types of datawithout departing from the invention and the requested data may be basedon known availability of different types of data at the entity location(e.g., if cameras are associated with each queue, image data may berequested, or the like).

At step 238, the instruction to capture real-time queue data may betransmitted to one or more image capture devices, such as image capturedevice 165. Although the instruction is shown as being transmitted toone device, the instruction may be transmitted to a plurality of deviceswithout departing from the invention.

At step 239, the instruction to capture real-time queue data may bereceived by the image capture device 165 and executed. Accordingly, oneor more images of one or more queues may be captured via the imagecapture device 165.

At step 240, the image capture device 165 may transmit the capturedimage data to the entity POS system 160. At step 241, the entity POSsystem 160 may receive the captured image data.

With reference to FIG. 2H, at step 242, the entity POS system 160 mayretrieve additional queue data. For instance, weight data associatedwith items on a conveyor belt may be retrieved for one or more queue,and the like. Additionally or alternatively, a location of the userwithin the entity (e.g., based on a location of the entity computingdevice 150 associated with the user) may be retrieved.

At step 243, real-time queue response data may be generated. Forinstance, the entity POS system 160 may compile the image data and anyother retrieved data (e.g., weight data, or the like) and generatedreal-time queue response data.

At step 244, the entity POS system 160 may transmit the real-time queueresponse data to the smart tracking computing platform 110.

At step 245, the real-time queue response data may be received andanalyzed by the smart tracking computing platform 110. For instance, amachine learning model trained using historical data including labeleddatasets associated with patterns or links between a number of customersin a queue, a time to complete checkout, a number of items in a queue, aweight of items in a queue, and the like, may receive the real-timequeue response data, as well as selected item data and/or location dataof the user, and analyze the data. The machine learning model may thenoutput an optimal queue. In some examples, the optimal queue may includethe queue that is likely to be closest to the current location of theuser and/or require the least amount of time to complete checkout. Insome examples, analyzing the real-time queue response data may includeanalyzing images captured to identify a number of users currently in oneor more queues, a number of items on a conveyor belt for one or morequeues, a size or shape of items in one or more queues, or the like.

In some examples, the machine learning model may also use as inputs oneor more personalized factors associated with the user. For instance,user profile data may indicate user preferences, user limitations, orthe like, that may be used, as desired by the user, in determining theoptimal queue.

At step 246, an optimal queue recommendation may be output by themachine learning model.

At step 247, a notification may be generated by the smart trackingcomputing platform 110. For instance, a notification providing a numberor other identifier associated with the optimal queue may be generated.FIG. 6 illustrates one example user interface 600 including anidentified optimal checkout recommendation. The interface may alsoinclude an option for the user to accept the identified optimal queue.This data may then be used to update and/or validate the machinelearning model.

With reference to FIG. 2I, at step 248, the generated notification maybe transmitted to the entity computing device 150 and displayed by theentity computing device 150. In some examples, the notification mayinclude selectable options for the user including, for instance, anoption to accept the recommendation or request a revised recommendation.

At step 249, the user may be detected at a queue within the entitylocation. For instance, the user may be detected (e.g., via a locationbeacon arranged at or near the queue, via image data captured at thequeue, via location tracking data obtained from the entity computingdevice 150 associated with the user, or the like) at the recommendedqueue (e.g., recommendation was accepted) or at a different queue (e.g.,recommendation was not accepted).

At step 250, the checkout for the user may be processed. In someexamples, payment for the selected items may be processed automatically(e.g., with limited or no user interaction) based on payment dataobtained from the user profile, via a mobile payment applicationexecuting on the user computing device 170 that is in communication withthe entity POS system 160, or the like. In some examples, a user may bepresented with a user interface requesting authorization to pay (e.g.,via the user computing device 170, via the entity POS system 160, viathe entity computing device 150, or the like). Further, in somearrangements, additional authentication data may be requested and/orprovided to process the checkout/payment.

At step 251, user checkout data or information may be generated by theentity POS system 160. For instance, user checkout data including itemspurchased, number of items purchased, amount of time from joining thequeue to checkout completion, or the like, may be generated.

At step 252, the user checkout data may be transmitted by the entity POSsystem to the user computing device 170. For instance, a record of thetransaction including the user checkout data may be transmitted to theuser computing device 170 and displayed by a display of the device.

At step 253, the user checkout data may be transmitted by the entity POSsystem 160 to the smart tracking computing platform 110.

With reference to FIG. 2J, at step 254, the user checkout data may bereceived by smart tracking computing platform 110 and used to updateand/or validate the machine learning model. For instance, informationrelated to whether the queue recommendation was accepted, time fromjoining queue to completion, number of items, and the like, may be usedto update and/or validate the machine learning model. Accordingly, themachine learning model may continuously be updated and accuracyimproved.

At step 255, in some examples, after completing the checkout process,the entity POS system (or smart tracking computing platform 110) may endthe connection between the user computing device 170 and the entitycomputing device 150. Accordingly, upon completing the transaction, thetwo devices might no longer be able to communicate in order to preventunintended data transmission. Accordingly, the connection or associationbetween the user computing device 170 or user associated therewith andthe entity computing device 150 may be temporary (e.g., only for thisparticular shopping event).

FIG. 3 is a flow chart illustrating one example method of implementingand using facial recognition for smart tracking and queue management inaccordance with one or more aspects described herein. The processesillustrated in FIG. 3 are merely some example processes and functions.The steps shown may be performed in the order shown, in a differentorder, more steps may be added, or one or more steps may be omitted,without departing from the invention. In some examples, one or moresteps may be performed simultaneously with other steps shown anddescribed. One of more steps shown in FIG. 3 may be performed inreal-time or near real-time.

At step 300, registration data may be received from a plurality ofusers. For instance, smart tracking computing platform 110 may receiveregistration data from a plurality of users. In some examples, theregistration data may include user identifying information, user deviceidentifying information, contact information, payment information (e.g.,credit card number, account number, or the like), image data,authentication data, and the like. In some examples, the registrationdata may be used to generate a user profile for each user.

At step 302, image data may be received. For instance, smart trackingcomputing platform 110 may receive image data captured by an imagecapture device 165. The image data may be captured in response todetection of a user device at an entity location.

At step 304, the image data may be analyzed using one or more facialrecognition techniques. The image data analysis may be performed todetermine whether the user is identifiable from the image data (e.g.,whether the user is a registered user).

At step 306, a determination may be made as to whether the user isidentifiable. If not, the process may end.

If, at step 306, the user is identifiable (e.g., facial recognitionanalysis of the image data indicates a registered user), at step 308,user profile data associated with the user may be retrieved. The userprofile data may include mobile device information associated with theuser, payment information, and the like.

At step 310, one or more smart tracking and/or queue managementfunctions may be initiated. For instance, one or more smart trackingfunctions that was previously disabled or deactivated may be enabled oractivated. In some examples, a user or user device may be linked to adevice, such as entity computing device 150, within the entity location,such as a shopping cart having an entity computing device 150 associatedtherewith. In some examples, a welcome message may be displayed by theentity computing device to indicate to the user that the user issuccessfully linked to the entity computing device.

At step 312, purchase item data may be received. For instance, as theuser traverses the entity location to acquire items for purchase,purchase item data captured via an entity computing device, such asentity computing device 150, may be received by the smart trackingcomputing platform 110. In some arrangements, as a user places an itemin the shopping cart, the item may be scanned and added to a list ofitems for purchase. That data may be transmitted to the smart trackingcomputing platform 110 for analysis. Additionally or alternatively,image data may be used to determine a number of items in the user'sshopping cart. This data may be transmitted to the smart trackingcomputing platform 110 for analysis. In some examples, weight sensors ona shopping cart may be used to determine a weight associated with itemsfor purchase and this data may be transmitted to the smart trackingcomputing platform 110 for analysis.

At step 314, the smart tracking computing platform 110 may receive arequest for checkout/optimal queue recommendation or identification. Forinstance, upon completion of a shopping trip, the user may requestgeneration of an optimal queue for checkout (e.g., via the entitycomputing device 150, user computing device 170, or the like).

At step 316, real-time queue data may be received. For instance, imagedata associated with one or more queues, weight data associated with oneor more queues, and the like, as well as location information associatedwith the user, may be received.

At step 318, the real-time queue data may be analyzed, e.g., using amachine learning model, to identify or recommend an optimal queue forcheckout. The optimal queue may be based on current user location,number of people identified in each queue, number of items each user hasin a queue, and the like.

At step 320, a notification including the identified optimal queue maybe generated and transmitted for display on entity computing device 150,user computing device 170, or the like.

The user may then proceed to the recommended queue for checkout locationdata associated with the user may be captured to determine whether theuser accepted the recommended queue. This feedback data may be used tofurther refine the one or more machine learning models used to identifythe optimal queue.

FIG. 4 is a flow chart illustrating one example method of implementingand using facial recognition for smart tracking and queue management inaccordance with one or more aspects described herein. The processesillustrated in FIG. 4 are merely some example processes and functions.The steps shown may be performed in the order shown, in a differentorder, more steps may be added, or one or more steps may be omitted,without departing from the invention. In some examples, one or moresteps may be performed simultaneously with other steps shown anddescribed. One of more steps shown in FIG. 4 may be performed inreal-time or near real-time.

At step 400, a user may be detected and image data may be captured andreceived by the smart tracking computing platform 110. For instance,upon entering an entity location, a user may be detected (e.g., a signalemitted from the user's mobile device may be detected, a user mobiledevice may detect a signal from a location beacon, a movement sensor maydetect a user, or the like). Upon detecting the user, image data may becaptured and transmitted to the smart tracking computing platform 110.The image data may include an image of a face of a user.

At step 402, the image data may be analyzed using facial recognitiontechniques to determine whether the user is a registered or recognizeduser.

At step 404, a determination may be made as to whether the user isidentifiable (e.g., a registered or recognized user). If the user is nota registered or recognized user, the process may end and the user mightnot receive smart tracking and optimized queue recommendations.

If, at step 404, the user is identifiable, at step 406, user profiledata associated with the user may be retrieved. For instance, userprofile data including user identifying information, mobile deviceinformation associated with the user, account or payment information forthe user, and the like, may be retrieved.

At step 408, a type of connection may be identified and/or received. Forinstance, a type of connection between the entity POS system 160 anduser computing device 170 (e.g., ultrawide band, Bluetooth™, cellular,or the like) or another device (e.g., entity computing device 150, orthe like) may be identified. The connection may be established upon theentity POS system detecting the user computing device 170 at the entitylocation.

At step 410, based on the identified type of connection, one or moreauthentication requirements may be retrieved and executed. In someexamples, the authentication requirements may be customizable by a userand retrieved from the user profile. Additionally or alternatively, theenterprise organization may determine different authenticationrequirements for different types of connections. Accordingly, a firsttype of connection may have different authentication requirements than asecond type of connection. After retrieving the authenticationrequirements, a request to authenticate may be transmitted to the userand authentication response data may be received. For instance, if afirst connection type is detected, the system may request a personalidentification number from the user. If a second connection type isidentified, a one-time pas scode transmitted to a pre-registered devicemay be requested. Various other arrangements for dynamic authenticationmay be used without departing from the invention.

At step 412, a determination may be made as to whether the user isauthenticated. For instance, authentication response data may becompared to pre-stored data (e.g., from the user profile) and, if thedata matches and, in at least some examples, the authenticationrequirements based on the type of connection are met, the user may beauthenticated.

If, at step 412, the user is not authenticated, a notificationindicating that the authentication data did not match and/or theauthentication requirements were not met may be generated andtransmitted to, for instance, user computing device 170, entity POSsystem 160, and the like. In some examples, the notification may requestadditional authentication data or to proceed without smart trackingfunctions.

If, at step 412, the user is authenticated, one or more smart trackingfunctions may be initiated or enabled. For instance, smart tracking forthe user may be initiated and an instruction to connect the user to anentity computing device may be generated.

At step 414, the instruction may be transmitted to the user computingdevice, entity POS system 160, or the like, and may cause a connectionto be established between the user computing device 170 and entitycomputing device 150 which may, for instance, be connected to a shoppingcart or other device to collect goods for purchase. The user may thenacquire items for purchase and the transaction may be processed. In someexamples, the user may complete the purchase transaction with limited orno interaction based on authentication data provided.

Accordingly, aspects described herein enable efficient purchasing ofitems with limited user interaction. For instance, by relying on facialrecognition to identify a user, the system may reduce or eliminate theneed for user identifying information to be provided. Further, bymodifying authentication requirements based on a communication protocolin use, the system may dynamically identify a most secure method forproviding data based on technology in use.

Further, aspects described herein enable efficient queue management andpayment processing by using facial recognition to identify a user,retrieve profile data and associated a user with a computing device. Theuser's items for purchase may be captured and real-time queue data maybe used to identify an optimal queue for the user, thereby reducing thelikelihood of an inefficient checkout experience.

FIG. 7 depicts an illustrative operating environment in which variousaspects of the present disclosure may be implemented in accordance withone or more example embodiments. Referring to FIG. 7 , computing systemenvironment 700 may be used according to one or more illustrativeembodiments. Computing system environment 700 is only one example of asuitable computing environment and is not intended to suggest anylimitation as to the scope of use or functionality contained in thedisclosure. Computing system environment 700 should not be interpretedas having any dependency or requirement relating to any one orcombination of components shown in illustrative computing systemenvironment 700.

Computing system environment 700 may include smart tracking computingdevice 701 having processor 703 for controlling overall operation ofsmart tracking computing device 701 and its associated components,including Random Access Memory (RAM) 705, Read-Only Memory (ROM) 707,communications module 709, and memory 715. Smart tracking computingdevice 701 may include a variety of computer readable media. Computerreadable media may be any available media that may be accessed by smarttracking computing device 701, may be non-transitory, and may includevolatile and nonvolatile, removable and non-removable media implementedin any method or technology for storage of information such ascomputer-readable instructions, object code, data structures, programmodules, or other data. Examples of computer readable media may includeRandom Access Memory (RAM), Read Only Memory (ROM), ElectronicallyErasable Programmable Read-Only Memory (EEPROM), flash memory or othermemory technology, Compact Disk Read-Only Memory (CD-ROM), DigitalVersatile Disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium that can be used to store the desired informationand that can be accessed by smart tracking computing device 701.

Although not required, various aspects described herein may be embodiedas a method, a data transfer system, or as a computer-readable mediumstoring computer-executable instructions. For example, acomputer-readable medium storing instructions to cause a processor toperform steps of a method in accordance with aspects of the disclosedembodiments is contemplated. For example, aspects of method stepsdisclosed herein may be executed on a processor on smart trackingcomputing device 701. Such a processor may execute computer-executableinstructions stored on a computer-readable medium.

Software may be stored within memory 715 and/or storage to provideinstructions to processor 703 for enabling smart tracking computingdevice 701 to perform various functions as discussed herein. Forexample, memory 715 may store software used by smart tracking computingdevice 701, such as operating system 717, application programs 719, andassociated database 721. Also, some or all of the computer executableinstructions for smart tracking computing device 701 may be embodied inhardware or firmware. Although not shown, RAM 705 may include one ormore applications representing the application data stored in RAM 705while smart tracking computing device 701 is on and correspondingsoftware applications (e.g., software tasks) are running on smarttracking computing device 701.

Communications module 709 may include a microphone, keypad, touchscreen, and/or stylus through which a user of smart tracking computingdevice 701 may provide input, and may also include one or more of aspeaker for providing audio output and a video display device forproviding textual, audiovisual and/or graphical output. Computing systemenvironment 700 may also include optical scanners (not shown).

Smart tracking computing device 701 may operate in a networkedenvironment supporting connections to one or more remote computingdevices, such as computing devices 741 and 751. Computing devices 741and 751 may be personal computing devices or servers that include any orall of the elements described above relative to smart tracking computingdevice 701.

The network connections depicted in FIG. 7 may include Local AreaNetwork (LAN) 725 and Wide Area Network (WAN) 729, as well as othernetworks. When used in a LAN networking environment, smart trackingcomputing device 701 may be connected to LAN 725 through a networkinterface or adapter in communications module 709. When used in a WANnetworking environment, smart tracking computing device 701 may includea modem in communications module 709 or other means for establishingcommunications over WAN 729, such as network 731 (e.g., public network,private network, Internet, intranet, and the like). The networkconnections shown are illustrative and other means of establishing acommunications link between the computing devices may be used. Variouswell-known protocols such as Transmission Control Protocol/InternetProtocol (TCP/IP), Ethernet, File Transfer Protocol (FTP), HypertextTransfer Protocol (HTTP) and the like may be used, and the system can beoperated in a client-server configuration to permit a user to retrieveweb pages from a web-based server.

The disclosure is operational with numerous other computing systemenvironments or configurations. Examples of computing systems,environments, and/or configurations that may be suitable for use withthe disclosed embodiments include, but are not limited to, personalcomputers (PCs), server computers, hand-held or laptop devices, smartphones, multiprocessor systems, microprocessor-based systems, set topboxes, programmable consumer electronics, network PCs, minicomputers,mainframe computers, distributed computing environments that include anyof the above systems or devices, and the like that are configured toperform the functions described herein.

One or more aspects of the disclosure may be embodied in computer-usabledata or computer-executable instructions, such as in one or more programmodules, executed by one or more computers or other devices to performthe operations described herein. Generally, program modules includeroutines, programs, objects, components, data structures, and the likethat perform particular tasks or implement particular abstract datatypes when executed by one or more processors in a computer or otherdata processing device. The computer-executable instructions may bestored as computer-readable instructions on a computer-readable mediumsuch as a hard disk, optical disk, removable storage media, solid-statememory, RAM, and the like. The functionality of the program modules maybe combined or distributed as desired in various embodiments. Inaddition, the functionality may be embodied in whole or in part infirmware or hardware equivalents, such as integrated circuits,Application-Specific Integrated Circuits (ASICs), Field ProgrammableGate Arrays (FPGA), and the like. Particular data structures may be usedto more effectively implement one or more aspects of the disclosure, andsuch data structures are contemplated to be within the scope of computerexecutable instructions and computer-usable data described herein.

Various aspects described herein may be embodied as a method, anapparatus, or as one or more computer-readable media storingcomputer-executable instructions. Accordingly, those aspects may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, an entirely firmware embodiment, or an embodiment combiningsoftware, hardware, and firmware aspects in any combination. Inaddition, various signals representing data or events as describedherein may be transferred between a source and a destination in the formof light or electromagnetic waves traveling through signal-conductingmedia such as metal wires, optical fibers, or wireless transmissionmedia (e.g., air or space). In general, the one or morecomputer-readable media may be and/or include one or more non-transitorycomputer-readable media.

As described herein, the various methods and acts may be operativeacross one or more computing servers and one or more networks. Thefunctionality may be distributed in any manner, or may be located in asingle computing device (e.g., a server, a client computer, and thelike). For example, in alternative embodiments, one or more of thecomputing platforms discussed above may be combined into a singlecomputing platform, and the various functions of each computing platformmay be performed by the single computing platform. In such arrangements,any and/or all of the above-discussed communications between computingplatforms may correspond to data being accessed, moved, modified,updated, and/or otherwise used by the single computing platform.Additionally or alternatively, one or more of the computing platformsdiscussed above may be implemented in one or more virtual machines thatare provided by one or more physical computing devices. In sucharrangements, the various functions of each computing platform may beperformed by the one or more virtual machines, and any and/or all of theabove-discussed communications between computing platforms maycorrespond to data being accessed, moved, modified, updated, and/orotherwise used by the one or more virtual machines.

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one or more of the steps depicted in theillustrative figures may be performed in other than the recited order,one or more steps described with respect to one figure may be used incombination with one or more steps described with respect to anotherfigure, and/or one or more depicted steps may be optional in accordancewith aspects of the disclosure.

What is claimed is:
 1. A computing platform, comprising: at least oneprocessor; a communication interface communicatively coupled to the atleast one processor; and a memory storing computer-readable instructionsthat, when executed by the at least one processor, cause the computingplatform to: receive, from an image capture device, an image of a userpresent at a location of an entity; retrieve, based on facialrecognition analysis of the image, user profile data; initiate smarttracking and queue management operations; receive, from a computingdevice associated with the user, items selected for purchase; receive,from the computing device, user input requesting an optimal checkoutqueue; receive, from a plurality of checkout queues, real-time queuedata; analyze the real-time queue data in real-time to identify anoptimal queue for the user, wherein the optimal queue for the user isidentified based, at least in part, on the items selected for purchase;generate a user interface instructing the user to proceed to theidentified optimal queue; transmit the user interface for display on thecomputing device; and cause the user interface to display on thecomputing device.
 2. The computing platform of claim 1, wherein thecomputing device associated with the user is a computing device of theentity that is temporarily associated with the user.
 3. The computingplatform of claim 1, wherein the real-time queue data includes imagedata from the plurality of checkout queues.
 4. The computing platform ofclaim 1, wherein analyzing the real-time queue data is performed byexecuting a machine learning model.
 5. The computing platform of claim4, further including instructions that, when executed, cause thecomputing platform to: receive user checkout information includingwhether the user accepted the instruction to proceed to the identifiedoptimal queue; and validate the machine learning model based on the usercheckout information.
 6. The computing platform of claim 5, wherein theuser checkout information including whether the user accepted theinstruction to proceed to the identified optimal queue is based, atleast in part, on current location data within the entity location ofthe computing device associated with the user.
 7. The computing platformof claim 1, wherein the optimal queue for the user is identified furtherbased on a distance between the user and the plurality of checkoutqueues and a number of users currently in each checkout queue of theplurality of checkout queues.
 8. A method, comprising: receiving, by acomputing platform, the computing platform having at least one processorand memory, and from an image capture device, an image of a user presentat a location of an entity; retrieving, by the at least one processorand based on facial recognition analysis of the image, user profiledata; initiating, by the at least one processor, smart tracking andqueue management operations; receiving, by the at least one processorand from a computing device associated with the user, items selected forpurchase; receiving, by the at least one processor and from thecomputing device, user input requesting an optimal checkout queue;receiving, by the at least one processor and from a plurality ofcheckout queues, real-time queue data; analyzing, by the at least oneprocessor, the real-time queue data in real-time to identify an optimalqueue for the user, wherein the optimal queue for the user is identifiedbased, at least in part, on the items selected for purchase; generating,by the at least one processor, a user interface instructing the user toproceed to the identified optimal queue; transmitting, by the at leastone processor, the user interface for display on the computing device;and causing, by the at least one processor, the user interface todisplay on the computing device.
 9. The method of claim 8, wherein thecomputing device associated with the user is a computing device of theentity that is temporarily associated with the user.
 10. The method ofclaim 8, wherein the real-time queue data includes image data from theplurality of checkout queues.
 11. The method of claim 8, whereinanalyzing the real-time queue data is performed by executing a machinelearning model.
 12. The method of claim 11, further including:receiving, by the at least one processor, user checkout informationincluding whether the user accepted the instruction to proceed to theidentified optimal queue; and validating, by the at least one processor,the machine learning model based on the user checkout information. 13.The method of claim 12, wherein the user checkout information includingwhether the user accepted the instruction to proceed to the identifiedoptimal queue is based, at least in part, on current location datawithin the entity location of the computing device associated with theuser.
 14. The method of claim 8, wherein the optimal queue for the useris identified further based on a distance between the user and theplurality of checkout queues and a number of users currently in eachcheckout queue of the plurality of checkout queues.
 15. One or morenon-transitory computer-readable media storing instructions that, whenexecuted by a computing platform comprising at least one processor,memory, and a communication interface, cause the computing platform to:receive, from an image capture device, an image of a user present at alocation of an entity; retrieve, based on facial recognition analysis ofthe image, user profile data; initiate smart tracking and queuemanagement operations; receive, from a computing device associated withthe user, items selected for purchase; queue; receive, from thecomputing device, user input requesting an optimal checkout receive,from a plurality of checkout queues, real-time queue data; analyze thereal-time queue data in real-time to identify an optimal queue for theuser, wherein the optimal queue for the user is identified based, atleast in part, on the items selected for purchase; queue; generate auser interface instructing the user to proceed to the identified optimaltransmit the user interface for display on the computing device; andcause the user interface to display on the computing device.
 16. The oneor more non-transitory computer-readable media of claim 15, wherein thecomputing device associated with the user is a computing device of theentity that is temporarily associated with the user.
 17. The one or morenon-transitory computer-readable media of claim 15, wherein thereal-time queue data includes image data from the plurality of checkoutqueues.
 18. The one or more non-transitory computer-readable media ofclaim 15, wherein analyzing the real-time queue data is performed byexecuting a machine learning model.
 19. The one or more non-transitorycomputer-readable media of claim 18, further including instructionsthat, when executed, cause the computing platform to: receive usercheckout information including whether the user accepted the instructionto proceed to the identified optimal queue; and validating the machinelearning model based on the user checkout information.
 20. The one ormore non-transitory computer-readable media of claim 19, wherein theuser checkout information including whether the user accepted theinstruction to proceed to the identified optimal queue is based, atleast in part, on current location data within the entity location ofthe computing device associated with the user.
 21. The one or morenon-transitory computer-readable media of claim 15, wherein the optimalqueue for the user is identified further based on a distance between theuser and the plurality of checkout queues and a number of userscurrently in each checkout queue of the plurality of checkout queues.